PKU Class 2024 Spring: Large Language Model in Decision Intelligence

Instructor: Kun Yuan (kunyuan@pku.edu.cn)

Teaching assistants:

• Yudong Bai (yutonghe@pku.edu.cn)
  
• Yunteng Geng (2301213081@pku.edu.cn)
  
• Yutong He (yutonghe@pku.edu.cn)
  
• Peijin Li (2301213056@stu.pku.edu.cn)
  
• Zihao Liu (2100011704@stu.pku.edu.cn)
  
• Keer Lu (2301213094@stu.pku.edu.cn)
  
• Yilong Song (2301213059@pku.edu.cn)
  
• Qianyou Sun (2301111049@stu.pku.edu.cn)
  
• Yuchi Wang (wangyuchi@stu.pku.edu.cn)
  

Office hour: 4pm - 5pm Wednesday, 静园六院220

References

Stanford CS224n: Natural Language Processing with Deep Learning

Lectrures

Lecture 1: Introduction to LLM

• Introduction to large language model [Slides]
• Reading:
  
  • Andrej Karpathy, State of GPT
    
  • Andrej Karpathy, The busy person’s intro to LLM
    

Lecture 2: Linear algebra and optimization

• Convex set; Convex functions; Gradient descent; Convergence [Slides]
  
• Notes: [Linear algebra] [Gradient descent]

Lecture 3: Basics in machine learning

• Linear regression; Logistic regression; Multi-classification; Neural network [Slides]
• Reading:
  
  • Stanford CS231n, Linear classification
    
  • Stanford CS231n, Neural netowrk part I

Lecture 4: Word embedding and language models

• Word embedding; [Slides]
• Language models; Recurrent neural network; (Slides are adapted from Stanford CS224n RNN)
• Back propogation in RNN [Slides]
• Sequence-to-sequence model (Slides are adapted from Stanford CS224n Seq2Seq)

Lecture 5: Transformer

• Forward-Backward propogation [Hand-written materials]
• Transformers (Slides are adapted from Stanford CS224n Transformers)
• Parameters and Computations in Transformers [Slides]
• Reading:
  
  • Illustrated Guide to Transformers Neural Network: A step by step explanation. [Youtube video] [Bilibili video]

Guest Lecture I:

• Large language model in mathematical reasoning (Dr. Jihai Zhang, Alibaba DAMO Academcy)

Lecture 6: Pretrain and Fine-tune Paradigm

• Teacher forcing; Pretrain; Fine-tune; BERT; GPTs [Slides]
• Reading:
  
  • J. Devlin et.al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  • A. Radford et.al., Improving Language Understanding by Generative Pre-Training
  • A. Radford et.al., Language models are unsupervised multitask learners
  • T. B. Brown et.al., Language Models are Few-Shot Learners

Lecture 7: Optimizers

• Stochastic gradient descent [Slides] [Notes]
• Momentum SGD [Slides]
• Adagrad; RMSProp; Adam [Slides]
• Memories in Transformer [Slides]
• Mixed-precision training [Slides]

Midterm Exam

• Midterm review [Slides]

Lecture 8: Distributed Training

• Scaling law [Slides]
• Data parallelism and communication saving; Pipeline parallelism; Tensor parallelism [Slides]

Lecture 9: Data Prepation

• Data source; Deduplication; Quality filtering; Sensitive information reduction; Data composition; Data curriculum [Slides]

Lecture 10: Principals in Prompt Engineering

• Pricipals in Prompt Engineering [Slides]
• Reading:
  
  • OpenAI, Prompt Engineering